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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
A SEMIAUTOMATIC ANOMALOUS CHANGE DETECTION METHOD FOR 
MONITORING AIMS 
G.Artese*, V. Achilli?, M. Fabris", M.Perrelli* 
* Land Planning Dept. - University of Calabria - Ponte Bucci cubo 45B - 87036 - Rende - Italy 
(g.artese, marcello.perrelli)@unical.it 
5 s9** Dept. - University of Padua - Via Marzolo ** 
Commission VII, WG VII/5 
KEY WORDS: Change Detection, Image Processing, Registration, Cultural Heritage, Camera Phone. 
ABSTRACT: 
In the framework of the development of a web site devoted to the documentation and monitoring of the cultural heritage (above all 
monumental buildings), a semiautomatic method for anomalous change detection has been set up. The method uses the grouping of 
the image difference values, to detect both small and diffused changes. Three tests are described to evaluate the performances of the 
method. The results show that good performances are obtained in case of cloudy days, while the presence of shadows requires the 
interpretation of an operator to distinguish true and false changes. 
1. INTRODUCTION 
In the framework of the development of a web site devoted to 
the documentation and monitoring of the cultural heritage 
(above all monumental buildings), with particular regard to 
emergency management, anomaly detection and early warning 
(Artese,G., Gencarelli,M., 2008), a semiautomatic method for 
anomalous change detection has been set up. The system uses 
images captured and sent by camera phones; these images are 
compared with archived ones to detect anomalous changes and, 
consequently, to activate an early warning procedure. 
The availability of high resolution digital cameras combined 
with the possibilities offered by today's computers, both for 
camera calibration and image processing, allows the execution 
of controls and monitoring by using change detection 
techniques, based on the comparison of frames acquired at 
different times. 
The use of these techniques is widespread in various sectors, 
ranging from security to traffic monitoring, to the processing of 
radiographic images, etc.. . 
The issues involved range from the calibration of the cameras 
used, to the feature extraction, registration, and actual change 
detection. 
Some authors have proposed techniques that do not require 
prior calibration and optimal resampling (registration) and make 
use of the classification process of pixels (Theiler, J., Perkins, S., 
2006). 
1.1 Calibration 
Many algorithms have been proposed for automatic calibration 
of the images. For example, 
Cronk et al. (Cronk, S., 2006) have proposed an effective 
method in the case of many converging acquisitions. Another 
possibility is to consider non-rigid geometric deformations, due 
to lens distortion; for such a case Arsigny et al. (Arsigny, V., 
2006) have proposed a general methodology to parameterize the 
deformation of the image with a finite number of rigid or affine 
components, while maintaining the reversibility of the global 
deformation. 
Considering the presence of flat surfaces and straight lines, 
different strategies can be followed. Habib et al. (Habib, AS, 
2002, Habib, AS, 2004, Habib, AS, 2005) have proposed a 
method for both calibration and registration, based on the use of 
straight lines. 
In the case of monitoring, internal and external orientation 
parameters are known, at least for a base image. 
In our work, straight lines were used during calibration of the 
camera lens to eliminate distortion, after having obtained the 
outlines with classical technique (Canny, J., 1986). 
1.2 Identification of interest points 
To obtain a good image registration, you must choose a set of 
interest points, which must be visible, and whose image 
coordinates must be known or measurable. For monitoring 
aims, one has in general the availability of one or more images 
in which interest points are detected through automatic or 
manual techniques. The classical operators Forstner (Forstner, 
W., 1987) Harris (Harris, CG, 1988) and Moravec (Moravec, 
HP, 1979) can be used effectively. In our work we used a 
semiautomatic technique, along with the operators of Harris and 
Moravec. 
1.3 Cross-correlation 
To perform registration, of fundamental importance for change 
detection, it is essential to find, in the images obtained at later 
dates, the interest points which were chosen in the base 
frames. For this purpose you can use the matching 
techniques. Among these, the cross-correlation is simple and 
effective (Jaehne, B., 1989). The cross-correlation allows to 
obtain the correspondence between two digital images, based on 
two assumptions: the images differ geometrically only due to a 
translation and radiometrically only for brightness and 
contrast. The accuracy of the cross-correlation decreases rapidly 
when the geometric assumptions are not met, especially when 
you have rotations greater than 20? or differences in scale 
greater than 30% (Forstner, W., 1984). 
The different perspectives of the images to compare cause both 
translations and rotations. For the buildings, it's in general